Word repetition in separate conversations for detecting a sign of cognitive decline

Abstract

A computer-implemented method for supporting detection of a sign of cognitive decline is disclosed. In the method, two or more sets of conversation data of a target individual are obtained. The method includes combining at least two sets of the conversation data corresponding to plural different days to generate a combined set and extracting one or more linguistic features that represent vocabulary richness from at least the combined set. A feature that characterizes word repetitiveness in conversations across the plural different days of the target individual is calculated from the one or more linguistic features. The feature that characterizes the word repetitiveness is output.

Description

STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINT INVENTOR[0001]“Grace period disclosures” were made public on Jan. 13, 2018 and Mar. 12, 2018, respectively, less than one year before the filing date of the present U.S. patent application. The publication was entitled “Word Repetition in Separate Conversations for Detecting Dementia: A Preliminary Evaluation on Data of Regular Monitoring Service” and the joint authors of this publication were Kaoru Shinkawa and Yasunori Yamada, who are also named as joint-inventors of the invention described and claimed in the present patent U.S. application. This publication was published at the web site of AMIA 2018 Informatics Summit (https: / / informaticssummit2018.zerista.com / event / member / 470366) on Jan. 13, 2018. An oral presentation based on this publication was also made by Ms. Kaoru Shinkawa as presenter on Mar. 12, 2018 at AMIA 2018 Informatics Summit held in San Francisco at Mar. 12-15, 2018.BACKGROUNDTechnical Field[0002]The...

AI Technical Summary

Benefits of technology

The method improves the detection of cognitive decline by leveraging the enriched features, reducing computational resources, and effectively utilizing varied conversation data, achieving higher discriminative power and accuracy compared to conventional features.

Problems solved by technology

As the worldwide elderly population increases, the incidence of dementia is becoming an increasingly serious health and social problem.

Although there are already several projects and services for monitoring the health of elderly persons with frequent data collection by using mobile applications, it has not been explored sufficiently how to exploit the data collected on a daily basis to detect dementia.

The short-term memory loss associated with dementia makes ordinary conversation difficult because of language dysfunctions such as word-finding and word-retrieval difficulties.

Syntactic complexity is closely associated with the incidence of dementia.

However, accuracy for estimating the risk of neurodegenerative diseases and cognitive decline is not enough.

Furthermore, it is expected to reduce the computational resources by enriching features that can be used to detect the sign of the cognitive decline.

In the typical monitoring service, practically, it is difficult to obtain conversation data at predetermined regular intervals due to user's circumstances.

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